But I think in general the idea OP was dismissing was generating synthetic data rather than attempting to anonymize prod data. In that case you have a risk of modeling the generator rather than your users.

The bottom line that if you want to draw insights from data, that particular data must make its way to the engineer unadulterated. If you de-risk some fields in your data via removal/masking/entropy, then you are excluding those fields from ml.

You are not talking about blinded machine learning, you're talking about not doing machine learning on sensitive data in the first place. The whole discussion is moot.